051130 VO Introductory Statistics (2024W)
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Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).
Details
Language: German
Examination dates
Lecturers
Classes (iCal) - next class is marked with N
- Monday 07.10. 18:30 - 21:00 Hörsaal 1, Währinger Straße 29 1.UG
- Monday 14.10. 18:30 - 21:00 Hörsaal 1, Währinger Straße 29 1.UG
- Monday 21.10. 18:30 - 21:00 Hörsaal 1, Währinger Straße 29 1.UG
- Monday 28.10. 18:30 - 21:00 Hörsaal 1, Währinger Straße 29 1.UG
- Monday 04.11. 12:30 - 14:45 UZA2 Hörsaal 6 (Raum 2Z227) 2.OG
- N Monday 11.11. 12:30 - 14:45 UZA2 Hörsaal 6 (Raum 2Z227) 2.OG
- Monday 18.11. 12:30 - 14:45 UZA2 Hörsaal 6 (Raum 2Z227) 2.OG
- Monday 25.11. 12:30 - 14:45 UZA2 Hörsaal 6 (Raum 2Z227) 2.OG
- Monday 02.12. 12:30 - 14:45 Hörsaal 1 Oskar-Morgenstern-Platz 1 Erdgeschoß
- Monday 09.12. 12:30 - 14:45 UZA2 Hörsaal 6 (Raum 2Z227) 2.OG
- Monday 16.12. 12:30 - 14:45 UZA2 Hörsaal 6 (Raum 2Z227) 2.OG
- Monday 13.01. 12:30 - 14:45 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
- Monday 20.01. 12:30 - 14:45 Hörsaal 42 Hauptgebäude, 2.Stock, Stiege 7
Information
Aims, contents and method of the course
Students have basic competences in descriptive and inferential statistics
Assessment and permitted materials
written exam
Minimum requirements and assessment criteria
50% of points in written exam
Examination topics
Descriptive and Exploratory Statistics
Representation of Distributions
Empirical Distribution Function and Quantiles
Descriptive Measures of Location and Spread
Additional Measures (Skewness, Kurtosis)
Association, Correlation
Probability Theory
Fundamentals of Probability Theory
Event Algebra, Basic Problems of Combinatorics
Conditional Probability and Independence
Law of Total Probability
Bayes' Theorem
Random Variables
Important Discrete Distributions
Important Continuous Distributions
Chebyshev's Inequality
Law of Large Numbers
Central Limit Theorem
Techniques of Inferential Statistics
Point Estimators
Interval Estimators
Hypothesis Testing
Classical Tests for Normal Distribution
Simple Analysis of Variance
Test for Independence
Checking Distribution Assumptions
Non-parametric Testing Procedures
Monte Carlo Methods
Monte Carlo Integration
Importance Sampling Methods
Causality
Rubin's Potential Outcomes Framework
do{} Operator
Causal Bayesian Networks
Entropy
Shannon Entropy
Mutual Information
Stochastic Processes
Markov Chains
Ergodicity and Stationarity
Limit Distribution
Representation of Distributions
Empirical Distribution Function and Quantiles
Descriptive Measures of Location and Spread
Additional Measures (Skewness, Kurtosis)
Association, Correlation
Probability Theory
Fundamentals of Probability Theory
Event Algebra, Basic Problems of Combinatorics
Conditional Probability and Independence
Law of Total Probability
Bayes' Theorem
Random Variables
Important Discrete Distributions
Important Continuous Distributions
Chebyshev's Inequality
Law of Large Numbers
Central Limit Theorem
Techniques of Inferential Statistics
Point Estimators
Interval Estimators
Hypothesis Testing
Classical Tests for Normal Distribution
Simple Analysis of Variance
Test for Independence
Checking Distribution Assumptions
Non-parametric Testing Procedures
Monte Carlo Methods
Monte Carlo Integration
Importance Sampling Methods
Causality
Rubin's Potential Outcomes Framework
do{} Operator
Causal Bayesian Networks
Entropy
Shannon Entropy
Mutual Information
Stochastic Processes
Markov Chains
Ergodicity and Stationarity
Limit Distribution
Reading list
* All of Statistics. Larry Wasserman, Springer, 2005.
* Statistical Data Analytics. W.Piegorsch, Wiley 2015.
* Statistik: Der Weg zur Datenanalyse. L. Fahrmeier, C. Heumann, R. Künstler, I. Pigeot, G. Tutz. Springer, 2016.
* Mathematics for Machine Learning, M.P. Deisenroth, A.A. Faisal, and C. Soon Ong. Cambridge University Press, 2020.
* Statistical Data Analytics. W.Piegorsch, Wiley 2015.
* Statistik: Der Weg zur Datenanalyse. L. Fahrmeier, C. Heumann, R. Künstler, I. Pigeot, G. Tutz. Springer, 2016.
* Mathematics for Machine Learning, M.P. Deisenroth, A.A. Faisal, and C. Soon Ong. Cambridge University Press, 2020.
Association in the course directory
Module: DAS EST UF-INF-12
Last modified: Tu 29.10.2024 15:05